Browsing by Author "Zhang, Rui"

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    A Bayesian Phase I/II Design to Determine Subgroup-Specific Optimal Dose for Immunotherapy Sequentially Combined with Radiotherapy
    (Wiley, 2023) Guo, Beibei; Zang, Yong; Lin, Li-Hsiang; Zhang, Rui; Biostatistics and Health Data Science, Richard M. Fairbanks School of Public Health
    Sequential administration of immunotherapy following radiotherapy (immunoRT) has attracted much attention in cancer research. Due to its unique feature that radiotherapy upregulates the expression of a predictive biomarker for immunotherapy, novel clinical trial designs are needed for immunoRT to identify patient subgroups and the optimal dose for each subgroup. In this article, we propose a Bayesian phase I/II design for immunotherapy administered after standard-dose radiotherapy for this purpose. We construct a latent subgroup membership variable and model it as a function of the baseline and pre-post radiotherapy change in the predictive biomarker measurements. Conditional on the latent subgroup membership of each patient, we jointly model the continuous immune response and the binary efficacy outcome using plateau models, and model toxicity using the equivalent toxicity score approach to account for toxicity grades. During the trial, based on accumulating data, we continuously update model estimates and adaptively randomize patients to admissible doses. Simulation studies and an illustrative trial application show that our design has good operating characteristics in terms of identifying both patient subgroups and the optimal dose for each subgroup.
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    A Modified Tumor-Node-Metastasis Classification for Primary Operable Colorectal Cancer
    (Oxford University Press, 2020-10-16) Zhang, Chundong; Mei, Zubing; Pei, Junpeng; Abe, Masanobu; Zeng, Xiantao; Huang, Qiao; Nishiyama, Kazuhiro; Akimoto, Naohiko; Haruki, Koichiro; Nan, Hongmei; Meyerhardt, Jeffrey A.; Zhang, Rui; Li, Xinxiang; Ogino, Shuji; Ugai, Tomotaka; Community and Global Health, School of Public Health
    Background: The American Joint Committee on Cancer (AJCC) 8th tumor-node-metastasis (TNM) classification for colorectal cancer (CRC) has limited ability to predict prognosis. Methods: We included 45 379 eligible stage I-III CRC patients from the Surveillance, Epidemiology, and End Results Program. Patients were randomly assigned individually to a training (n = 31 772) or an internal validation cohort (n = 13 607). External validation was performed in 10 902 additional patients. Patients were divided according to T and N stage permutations. Survival analyses were conducted by a Cox proportional hazard model and Kaplan-Meier analysis, with T1N0 as the reference. Area under receiver operating characteristic curve and Akaike information criteria were applied for prognostic discrimination and model fitting, respectively. Clinical benefits were further assessed by decision curve analyses. Results: We created a modified TNM (mTNM) classification: stages I (T1-2N0-1a); IIA (T1N1b, T2N1b, T3N0); IIB (T1-2N2a-2b, T3N1a-1b, T4aN0); IIC (T3N2a, T4aN1a-2a, T4bN0); IIIA (T3N2b, T4bN1a); IIIB (T4aN2b, T4bN1b); and IIIC (T4bN2a-2b). In the internal validation cohort, compared with the AJCC 8th TNM classification, the mTNM classification showed superior prognostic discrimination (area under receiver operating characteristic curve = 0.675 vs 0.667, respectively; 2-sided P < .001) and better model fitting (Akaike information criteria = 70 937 vs 71 238, respectively). Similar findings were obtained in the external validation cohort. Decision curve analyses revealed that the mTNM had superior net benefits over the AJCC 8th TNM classification in the internal and external validation cohorts. Conclusions: The mTNM classification provides better prognostic discrimination than AJCC 8th TNM classification, with good applicability in various populations and settings, to help better stratify stage I-III CRC patients into prognostic groups.
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    Crop yield and soil organic carbon under ridge–furrow cultivation in China: A meta-analysis
    (Wiley, 2021-06) Wang, Yunqi; Gao, Fuli; Wang, Lixin; Guo, Tongji; Qi, Liuran; Zeng, Huanyu; Liang, Yuexin; Zhang, Kai; Jia, Zhikuan; Zhang, Rui; Earth Sciences, School of Science
    Ridge–furrow cultivation (RF) is a popular emerging technique that can increase crop productivity in dry areas. However, the efficacy of RF on crop yield and soil organic carbon (SOC) remains uncertain under different climate and management conditions. Here, we compiled data from 48 publications to evaluate the response of yield and SOC to RF in China. Overall, our meta-analysis showed that RF increased yield by 30.2%, but it had no effects on SOC. When differentiated based on different categories, yield and SOC varied by crop species, climate, soil textures, mulching management, and ridge–furrow patterns. RF increased the yield of wheat, maize, soybean, rape, linseed, potato, and SOC under soybean cultivation. Yield increase with RF was also consistent across temperature and precipitation. Yield increase was observed in all the soil textures. There were no RF effects on SOC under different soil textures. RF enhanced yields under no mulching, straw mulching and plastic film mulching, but increased SOC only in combination with straw mulching. A higher yield increase was observed under alternating small and large ridges (ASLR) than alternating ridges and furrows (AR). RF decreased SOC by 11.7% under AR, but had no effects on SOC under ASLR. Together, ASLR with straw mulching could increase yield and SOC in coarse soil texture regions with annual mean temperature >10°C and annual mean precipitation > 400 mm. This study showed the importance of considering local environmental conditions with management practices in identifying appropriate RF practices for improving crop productivity and soil carbon sequestration.
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    IndoorWaze: A Crowdsourcing-Based Context-Aware Indoor Navigation System
    (IEEE, 2020-05) Li, Tao; Han, Dianqi; Chen, Yimin; Zhang, Rui; Zhang, Yanchao; Hedgpeth, Terri; Computer Information and Graphics Technology, School of Engineering and Technology
    Indoor navigation systems are very useful in large complex indoor environments such as shopping malls. Current systems focus on improving indoor localization accuracy and must be combined with an accurate labeled floor plan to provide usable indoor navigation services. Such labeled floor plans are often unavailable or involve a prohibitive cost to manually obtain. In this paper, we present IndoorWaze, a novel crowdsourcing-based context-aware indoor navigation system that can automatically generate an accurate context-aware floor plan with labeled indoor POIs for the first time in literature. IndoorWaze combines the Wi-Fi fingerprints of indoor walkers with the Wi-Fi fingerprints and POI labels provided by POI employees to produce a high-fidelity labeled floor plan. As a lightweight crowdsourcing-based system, IndoorWaze involves very little effort from indoor walkers and POI employees. We prototype IndoorWaze on Android smartphones and evaluate it in a large shopping mall. Our results show that IndoorWaze can generate a high-fidelity labeled floor plan, in which all the stores are correctly labeled and arranged, all the pathways and crossings are correctly shown, and the median estimation error for the store dimension is below 12%.
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    Meta-analysis of ridge-furrow cultivation effects on maize production and water use efficiency
    (Elsevier, 2020-05) Wang, Yunqi; Guo, Tongji; Qi, Liuran; Zeng, Huanyu; Liang, Yuexin; Wei, Shikun; Gao, Fuli; Wang, Lixin; Zhang, Rui; Jia, Zhikuan; Earth Sciences, School of Science
    Ridge-furrow cultivation (RF) is a popular dryland agricultural technique in China, but its effects on maize yield, total water consumption during crop growing stage (ET), and water use efficiency (WUE) have not been systematically analyzed. Here we conducted a meta-analysis of the RF effects on maize yield, ET and WUE based on the data collected from peer-reviewed literature. Yield, ET and WUE varied with climate, soil and mulching management. Averaged across all the geographic locations, RF increased the yield and WUE of maize by 47 % and 39 %, respectively, but no effects on ET. An increase in the yield and WUE occurred under RF in regions regardless of the mean growing season air temperature (MT) or a mean precipitation during the growing season (MP), although there was a trend that RF is more beneficial under low MP. RF also decreased ET in regions with MT>12 °C. RF increased the yield and WUE in regions with medium or fine soil texture. RF increased the yield, ET, and WUE in regions with low soil bulk density (BD) (≤1.3 g cm−3). But in areas where BD is larger than 1.3 g cm−3, RF only increased the yield and WUE. RF increased the yield and WUE with or without mulching, but decreased ET when no mulching was used. Together, optimizing RF effects on the yield, ET and WUE in maize was largely dependent on environmental conditions and management practices.